To increase machine and operator safety, particularly in poor visibility conditions, special sensors such as cameras, laser, and radar have been introduced to certain vehicles, for either on-highway or off-highway applications. An example of the sensor systems is one capable of detecting the presence of a visibility-degraded condition, such as for example, fog, and estimating visibility ranges.
This sensor system can function as a driving aid. For example, in fog, humans, based on naked eyesight alone, tend to overestimate visibility ranges (see Cavallo et al., Hum. Factors 43: 442-451 (2001)), which may lead to excessive driving speeds or other risky driving maneuvers. A sensor system that provides a more accurate visibility range estimation in real time would serve to inform the vehicle operator of visibility degradation and could further include a control strategy to restrict the speed or other potentially risky driving maneuvers based on the detected or estimated condition.
Busch and Debes, IEEE Intelligent Sys., November/December 1998, 66-71, described a method based on a B-spline wavelet transform for analyzing video frames used in traffic-control systems and deriving a visibility range measure with extended signal-analysis techniques. Their system can be used to inform vehicle operators in real time of the presence of fog through VMS located at a distance away from the foggy area. However, this system relies on a fixed or static camera, not suitable for on-board applications on a moving vehicle. Further, the use of a single camera does not allow direct measurement of image depth. Thus, the Busch and Debes method also includes selecting a Region of Interest (ROI) in the digital images obtained by the fixed camera and assuming that all the pixels inside the ROI lie in one plane (e.g., a flat road) so as to conduct an inverse geometric transform. Busch and Debes' use of a video sequence may also be problematic, because from frame to frame certain undesired objects can vary greatly, making the selection of the ROI difficult.
Hautière et al., Machine Vision and Applications, 17(1): 8-20 (2006), reported a fog-detection and visibility-estimation system using an on-board camera. This system uses a preprocessing procedure by means of region growing, which allows for the determination of the most pertinent objects or pixels in the images obtained by the on-board camera. The most pertinent objects are then analyzed with a computation model based on Koschmieder's model and Duntley's attenuation law of atmospheric contrasts. Similarly, this single-camera system does not provide direct measurement of image depth, and therefore, the Hautière et al. approach also adopts the hypothesis of a flat road.
However, road conditions can vary greatly, particularly in off-highway applications. Thus, there remains a need for a more robust and accurate method for estimating visibility range in real time under a variety of circumstances, such as for example on-board use on vehicles for either on-highway or off-highway applications or both, or on-board use on other vessels or equipment, wherever a need for real-time estimation of visibility range exists.
The present disclosure is directed to addressing this need and other problems described above.